lean6sigma project template
TRANSCRIPT
[PROJECT]
[NAME]
LEAN SIX SIGMA BLACK BELT
DMAIC
DEFINE
MEASURE
ANALYSE
IMPROVE
CONTROL
Identify Customer Problem
Translate to Practical Problem
Translate to statistical problem
Identify Statistical solution
Translate to practical solution
1. What is the Project?2. Define measurement system3. Validate measure-ment system
10. Validate measurement system (X)
12. Implement process controls
4. Actual process performance
5. Define statistical success
6. Identify causes of defects
7. Determine vital causes
8. Define optimal settings
9. Define tolerance limits
11. Determine new process capability
X & Y
• Y is the outcome of the process.
• X are factors that influence Y
• Usually the project starts with an Y that is not very specific. This is good for discussion but not for a Lean Six Sigma Project.
• Therefore the so called “external” Y needs to be translated to an “internal” Y that is specific, concrete and measurable (slice the project)
• Use the Voice of the Customer (VOC) to capture the requirements
EXAMPLE
• Passengers are not satisfied with public transport.
• External Y = Public transport is not good
• You try to make this a bit more specific:
• Possible technique: Pareto, research
• The biggest problem is:
• Internal Y = the bus is often not on time
Not on time
No place t
o sit
Unfrien
dly sta
ff
Too noisy
Dirty
Too ex
pensiv
e
Not safe
too hot
too cold
unfrien
dly pass
enge
rs0
200
400
600
800
1000
1200
1400
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
CountCumulative %
STEP 1: PROJECT CHARTER (WHAT IS THE PROJECT?)
DEFINE
Business Case Process start and finish
[Short description of the process. How should it work (which is the customer requirement) and why is this process so important?]
[Clearly define where the process you want to improve starts and where it ends.]
Problem Statement In Scope and Out of Scope
[Where does the process deviate from the customer requirement, and why is this a problem?]
[Hint: don’t make it too big!]
Goal Statement Expected Benefits
[Short description of the improvement target.Examples:Shorten throughput time with xx hourReduce number of errors with xx%]
Don’t do it if there is not enough benefit!
SIPOC DIAGRAMSuppliers
• Raw materials• Sources• Manufacturers• Suppliers
Inputs
• Manpower• Resources• Equipment
Process Outputs
• Product• Timely
delivery• Increased
quality
Customers
• Young people• Students• Service
holders
Requirements
• Customer Satisfaction
• Expected quality
• Reduced Backlog
Look for new
customer
segment
Find custom
er needs
Identify critical needs
Develop prototy
pe
Test prototype & go
to product
ion
STEP 1: PROCESS DESCRIPTION OF THE PROCESS TO IMPROVE
DEFINE
Champion: [Name sponsor]
Process owner: [Name]
Sr. employees: [Members project]
Financial Analyst: [Name]
Master Black Belt: [Name]
Black Belt: [Name]
STEP 2: PROJECT TEAMDEFINE
STEP 2: MEASURABLE CCR + SPECIFICATIONS
Unit: [Output of the Process.
Example: a cookie from a cookie factory]
Chance: [Number of possible defects
Example: 2 (see below)]
Defect: [What leads to an unhappy customer?
Example: a broken cookie, a cookie without a peanut]
Unit of Measure: [Unit in which the output is measured
Example: broken yes/no
peanut: yes/no]
MEASURE
STEP 2: MEASUREMENT SYSTEMMEASURE
Definition of the data
How is the measurement unit determined?
How is the data collected?
Automated or manual?Who collects the data?
Frequency and collection dates?
How will the data be used?
Test hypothesisRoot-cause analysis
How will the data be presented?
Pareto, Histogram,Control Chart, Probability
plot,Box plot, …
STEP 3: VALIDATION OF THE MEASUREMENT SYSTEM
• [Gage R&R or Kappa test]
• or
• [The data is currently being used as source for management info and both the Process Owner and the Campion recognize this as representative for the process.]
MEASURE
Conclusion: We have a reliable Measurement System
STEP 4: CURRENT PERFORMANCE
• [Control Chart, example I-Chart (Control charts, individuals)]
• Probability plot (Graph)
Observation
Indiv
idual V
alu
e
332925211713951
0,18
0,17
0,16
0,15
0,14
0,13
0,12
0,11
0,10
_X=0,13555
UCL=0,16408
LCL=0,10703
1
1
I Chart of Proces X
ANALYZE
Conclusions: The data is representative for the process. The data is normally distributed. (or not)
Proces X
Perc
ent
0,180,170,160,150,140,130,120,110,100,09
99
95
90
80
70
605040
30
20
10
5
1
Mean 0,1356StDev 0,01326N 35AD 0,936P-Value 0,016
Probability Plot of Proces XNormal - 95% CI
STEP 4: CURRENT CAPABILITY OF THE PROCESS
• Process capability (Quality tools, capability analysis, normal)
We have a target that currenty is not met. Most data should be between LSL and USL.Conclusion: We have a problem.
ANALYZE
0,1620,1440,1260,1080,0900,072
LSL USL
LSL 0,06Target *USL 0,1Sample Mean 0,135554Sample N 35StDev(Within) 0,0133565StDev(Overall) 0,0133565
Process Data
Cp 0,50CPL 1,89CPU -0,89Cpk -0,89
Pp 0,50PPL 1,89PPU -0,89Ppk -0,89Cpm *
Overall Capability
Potential (Within) Capability
PPM < LSL 0,00PPM > USL 1000000,00PPM Total 1000000,00
Observed PerformancePPM < LSL 0,01PPM > USL 996115,04PPM Total 996115,05
Exp. Within PerformancePPM < LSL 0,01PPM > USL 996115,04PPM Total 996115,05
Exp. Overall Performance
WithinOverall
Process Capability of Proces X
Note: if the data is not normally distributed, only these parts are relevant.
STEP 5: STATISTIC SUCCESS
• Compare Process capability of department 1 (to be improved)
• With Process capability of department 2 (is performing better)
0,1620,1440,1260,1080,0900,072
LSL USL
LSL 0,06
Target *USL 0,1
Sample Mean 0,135554Sample N 35
StDev(Within) 0,0132909
StDev(Overall) 0,0133565
Process Data
Cp 0,50
CPL 1,89CPU -0,89
Cpk -0,89
Pp 0,50
PPL 1,89
PPU -0,89Ppk -0,89
Cpm *
Overall Capability
Potential (Within) Capability
PPM < LSL 0,00
PPM > USL 1000000,00PPM Total 1000000,00
Observed Performance
PPM < LSL 0,01
PPM > USL 996264,09PPM Total 996264,09
Exp. Within Performance
PPM < LSL 0,01
PPM > USL 996115,04PPM Total 996115,05
Exp. Overall Performance
WithinOverall
Process Capability of Amsterdam
ANALYZE
0,140,120,100,080,060,04
LSL USL
LSL 0,06
Target *USL 0,1
Sample Mean 0,0899916Sample N 35
StDev(Within) 0,0216307
StDev(Overall) 0,0216307
Process Data
Cp 0,31
CPL 0,46CPU 0,15
Cpk 0,15
Pp 0,31
PPL 0,46
PPU 0,15Ppk 0,15
Cpm *
Overall Capability
Potential (Within) Capability
PPM < LSL 28571,43
PPM > USL 257142,86PPM Total 285714,29
Observed Performance
PPM < LSL 82792,73
PPM > USL 321791,34PPM Total 404584,07
Exp. Within Performance
PPM < LSL 82792,73
PPM > USL 321791,34PPM Total 404584,07
Exp. Overall Performance
WithinOverall
Process Capability of Leeuwarden
Based on this analysis we hope to conclude that department is a suitable benchmark
STEP 6: INVENTORY OF CAUSES
We have measured two (or more) different teams/species/types/whatever, and we have measured a specific thing like: size, throughput time, number of failures, etc.
Now we want to know which factors could explain the difference between these teams/types/etc.
Example: Why is team A making far less mistakes than team B?
First make a long list of all causes (we call this a list of X’s). Use brainstorming and/or data-analysis
Then shorten the list until max 8 major causes remain.
Use techniques like: common sense, knowledge, experience and hypothesis testing.
ANALYZE
STEP 6: HYPOTHESIS TESTING, X=SOME-CAUSE
ANALYZE
Hypothesis: For Some-cause there is no difference between X en Y.
Conclusion: Some-cause is a relevant X. (or not)
Show that there is or is not a statistically significant difference in distribution and average between X and Y.Thus you need a similar row of data of X and Y.
Verify that data (I-chart, probability plot).
Check which test needs to be done:• For spread • and for average.
Note: these tests depend on; is Y discrete or continuous? Is X discrete or continuous? And do we have 2 or more groups of X?
STEP 6: SUMMARY OF CONCLUSIONS
Max 8 important X’s1. PQR2. STU3. VWZ4. ..5. ..6. ..7. ..8. ..
ANALYZE
Result: data: Short list of possible causes, max 8Process map: most critical process steps (focus)
STEP 7: DETERMINE THE VITAL FEW ROOT CAUSES
IMPROVE
From 8 important to (max) 3 root causes!!
using Lean techniques and hypothesis testing
HYPOTHESIS TESTING DECISION TREE
Internal Y
Discrete data for Y
Continuous data for X
Logistic Regression
Discrete data for X
Chi Square-
test
Continuous data for Y
Continuous data for X Regression
Discrete data for X
Mean problem
1 group of data for X
2 groups of data for
XMore
groups of data for X
Variance problem
2 groups of data for
XMore
groups of data for X
Normal:1-Way Anova
Not-normal:Kruskal-Wallis test
Normal:2-Sample t-test
Not-normal:Mann-Whitney test
Normal:F-test
Not-normal:Levene’s test
Normal:Bartlett’s test
Not-normal:Levene’s test
Normal:1-Sample t-test
Not-normal:1-Sample Wilcoxon test
STEP 7: SUMMARY OF CONCLUSIONSIMPROVE
3 root causes (X’s)
1. Root cause 12. Root cause 23. Root cause 3
STEP 8: DESIGN IMPROVEMENTS PER ROOT CAUSE
Determine per root cause the optimal setting!
IMPROVE
Root Cause Optimal solution
STEP 9: DEVELOP PRACTICAL SOLUTIONS PER ROOT CAUSE
IMPROVE
Root cause
Practical solution
Test method (step 11)
Effect costs conclusion
Pre requisites
STEP 10: EVERYONE IN THE PROCESS KNOWS THE NEW WAY OF WORKING, AND IS CAPABLE OF DOING IT
CONTROL
Prove that everyone understands the new method using the new work instructions or the Standard Operating Procedures (SOP).We prove that now and in the future we can rely on our measurement system by performing an analysis on the main X's.
LTL corrected
Target UTL corrected
Unit of measurement
Gage R&R in %
Procedure/Sop nr
remarks
X1
X2
X3
X4
STEP 11: CALCULATE NEW “PROCESS CAPABILITY”
CONTROL
Conclusion: success on short term has been proven!
0,150,140,130,120,110,10
LSL USL
LSL 0,1Target *USL 0,14Sample Mean 0,131175Sample N 35StDev(Within) 0,00841327StDev(Overall) 0,00841327
Process Data
Cp 0,79CPL 1,24CPU 0,35Cpk 0,35
Pp 0,79PPL 1,24PPU 0,35Ppk 0,35Cpm *
Overall Capability
Potential (Within) Capability
PPM < LSL 0,00PPM > USL 200000,00PPM Total 200000,00
Observed PerformancePPM < LSL 105,51PPM > USL 147103,66PPM Total 147209,17
Exp. Within PerformancePPM < LSL 105,51PPM > USL 147103,66PPM Total 147209,17
Exp. Overall Performance
WithinOverall
Process Capability of Amsterdam
Observation
Indiv
idual V
alu
e
332925211713951
0,16
0,15
0,14
0,13
0,12
0,11
_X=0,13118
UCL=0,15435
LCL=0,10800
I Chart of Amsterdam
Amsterdam
Perc
ent
0,160,150,140,130,120,110,10
99
95
90
80
70
605040
30
20
10
5
1
Mean 0,1312StDev 0,008352N 35AD 0,285P-Value 0,607
Probability Plot of AmsterdamNormal - 95% CI
STEP 12: IMPLEMENTATION PROCESS CONTROL
CONTROL
Conclusion: success on longer term is guaranteed!
Handover project + sign off+ thanks team
Root cause Solution Assurance